library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)
library(sf)
library(tmap)
library(mapview)
# read in data
us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>%
clean_names()
-make the description all lowercase -only keep observations where “description” variable contains “consumption” -remove any observations where “description” varibale contain “total”
renew_clean <- us_renew %>%
mutate(description = str_to_lower(description)) %>%
filter(str_detect(description, pattern = "consumption")) %>%
filter(!str_detect(description, pattern = "total"))
renew_date <- renew_clean %>%
mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>%
mutate(month_sep = yearmonth(yr_mo_day)) %>%
mutate(value = as.numeric(value)) %>%
drop_na(month_sep, value)
### Make a version where I have the month & year in separate columns
renew_parsed <- renew_date %>%
mutate(month = month(yr_mo_day, label = TRUE)) %>%
mutate(year = year(yr_mo_day))
renew_gg <- ggplot(data = renew_date, aes(x = month_sep, y = value, group = description)) +
geom_line(aes(color = description))
renew_gg
Updating my colors with paletteer palettes:
renew_gg +
scale_color_paletteer_d("palettetown::pichu")
renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)
Let’s look at our time series data in a couple different ways
renew_ts %>% autoplot(value)
renew_ts %>% gg_subseries(value)
# renew_ts %>% gg_season(value)
ggplot(data = renew_parsed, aes(x = month, y = value, group = year)) +
geom_line(aes(color = year)) +
facet_wrap(~description,
ncol = 1,
scales = "free",
strip.position = "right")
hydro_ts <- renew_ts %>%
filter(description == "hydroelectric power consumption")
hydro_ts %>% autoplot(value)
hydro_ts %>% gg_subseries(value)
# hydro_ts %>% gg_season(value)
ggplot(hydro_ts, aes(x = month, y = value, group = year)) +
geom_line(aes(color = year))
hydro_quarterly <- hydro_ts %>%
index_by(year_qu = ~(yearquarter(.))) %>%
summarize(avg_consumption = mean(value))
dcmp <- hydro_ts %>%
model(STL(value ~ season(window = 5)))
components(dcmp) %>% autoplot()
hist(components(dcmp)$remainder)
Now look at the ACF:
hydro_ts %>%
ACF(value) %>%
autoplot()
hydro_model <- hydro_ts %>%
model(ARIMA(value)) %>%
fabletools:: forecast(h = "4 years")
hydro_model %>% autoplot(filter(hydro_ts, year(month_sep) > 2010))
world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
layer = "TM_WORLD_BORDERS_SIMPL-0.3")
# mapview(world)